# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import random

import numpy as np
import pytest
import torch
from tensordict import TensorDict

from verl import DataProto
from verl.protocol import union_numpy_dict, union_tensor_dict


def test_union_tensor_dict():
    obs = torch.randn(100, 10)

    data1 = TensorDict({"obs": obs, "act": torch.randn(100, 3)}, batch_size=[100])
    data2 = TensorDict({"obs": obs, "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100])

    data_with_copied_obs = TensorDict(
        {"obs": obs.clone(), "next_obs": torch.randn(100, 10), "rew": torch.randn(100)}, batch_size=[100]
    )

    data = union_tensor_dict(data1, data2)
    with pytest.raises(AssertionError):
        data = union_tensor_dict(data1, data_with_copied_obs)

    data = np.random.random(100)
    data2 = [float("nan") for _ in range(99)]
    data2.append("nan")
    data2 = np.array(data2, dtype=object)
    data3 = np.tile(data2, (2, 1))
    a = {"a": data, "b": data2, "c": data3}
    b = {"a": data, "b": data2, "c": data3}
    b_ = {"a": np.random.random(100)}
    union_numpy_dict(a, b)
    with pytest.raises(AssertionError):
        union_numpy_dict(a, b_)


def test_tensor_dict_constructor():
    obs = torch.randn(100, 10)
    act = torch.randn(100, 10, 3)
    data = DataProto.from_dict(tensors={"obs": obs, "act": act})

    assert data.batch.batch_size == torch.Size([100])

    with pytest.raises(AssertionError):
        data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=2)

    with pytest.raises(AssertionError):
        data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=3)


def test_tensor_dict_make_iterator():
    obs = torch.randn(100, 10)
    labels = [random.choice(["abc", "cde"]) for _ in range(100)]
    dataset = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels})

    data_iter_1 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1)
    data_list_1 = []
    for data in data_iter_1:
        data_list_1.append(data)

    data_iter_2 = dataset.make_iterator(mini_batch_size=10, epochs=2, seed=1)
    data_list_2 = []
    for data in data_iter_2:
        data_list_2.append(data)

    for data1, data2 in zip(data_list_1, data_list_2):
        assert isinstance(data1, DataProto)
        assert isinstance(data2, DataProto)
        result = torch.all(torch.eq(data1.batch["obs"], data2.batch["obs"]))
        if not result.item():
            print(data1.batch["obs"])
            print(data2.batch["obs"])
            raise AssertionError()
        non_tensor_result = np.all(np.equal(data1.non_tensor_batch["labels"], data2.non_tensor_batch["labels"]))
        if not non_tensor_result.item():
            print(data1.non_tensor_batch["labels"])
            print(data2.non_tensor_batch["labels"])


def test_reorder():
    obs = torch.tensor([1, 2, 3, 4, 5, 6])
    labels = ["a", "b", "c", "d", "e", "f"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"})
    data.reorder(torch.tensor([3, 4, 2, 0, 1, 5]))

    assert torch.all(torch.eq(data.batch["obs"], torch.tensor([4, 5, 3, 1, 2, 6])))
    assert np.all(data.non_tensor_batch["labels"] == np.array(["d", "e", "c", "a", "b", "f"]))
    assert data.meta_info == {"name": "abdce"}


def test_chunk_concat():
    obs = torch.tensor([1, 2, 3, 4, 5, 6])
    labels = ["a", "b", "c", "d", "e", "f"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"name": "abdce"})

    with pytest.raises(AssertionError):
        data.chunk(5)

    data_split = data.chunk(2)
    assert len(data_split) == 2
    assert torch.all(torch.eq(data_split[0].batch["obs"], torch.tensor([1, 2, 3])))
    assert np.all(data_split[0].non_tensor_batch["labels"] == np.array(["a", "b", "c"]))
    assert data_split[0].meta_info == {"name": "abdce"}

    assert torch.all(torch.eq(data_split[1].batch["obs"], torch.tensor([4, 5, 6])))
    assert np.all(data_split[1].non_tensor_batch["labels"] == np.array(["d", "e", "f"]))
    assert data_split[1].meta_info == {"name": "abdce"}

    concat_data = DataProto.concat(data_split)
    assert torch.all(torch.eq(concat_data.batch["obs"], data.batch["obs"]))
    assert np.all(concat_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"])
    assert concat_data.meta_info == data.meta_info


def test_pop():
    obs = torch.randn(100, 10)
    act = torch.randn(100, 3)
    dataset = DataProto.from_dict({"obs": obs, "act": act}, meta_info={"2": 2, "1": 1})
    poped_dataset = dataset.pop(batch_keys=["obs"], meta_info_keys=["2"])

    assert poped_dataset.batch.keys() == {"obs"}
    assert poped_dataset.meta_info.keys() == {"2"}

    assert dataset.batch.keys() == {"act"}
    assert dataset.meta_info.keys() == {"1"}


def test_repeat():
    # Create a DataProto object with some batch and non-tensor data
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    # Test interleave=True
    repeated_data_interleave = data.repeat(repeat_times=2, interleave=True)
    expected_obs_interleave = torch.tensor([[1, 2], [1, 2], [3, 4], [3, 4], [5, 6], [5, 6]])
    expected_labels_interleave = ["a", "a", "b", "b", "c", "c"]

    assert torch.all(torch.eq(repeated_data_interleave.batch["obs"], expected_obs_interleave))
    assert (repeated_data_interleave.non_tensor_batch["labels"] == expected_labels_interleave).all()
    assert repeated_data_interleave.meta_info == {"info": "test_info"}

    # Test interleave=False
    repeated_data_no_interleave = data.repeat(repeat_times=2, interleave=False)
    expected_obs_no_interleave = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6]])
    expected_labels_no_interleave = ["a", "b", "c", "a", "b", "c"]

    assert torch.all(torch.eq(repeated_data_no_interleave.batch["obs"], expected_obs_no_interleave))
    assert (repeated_data_no_interleave.non_tensor_batch["labels"] == expected_labels_no_interleave).all()
    assert repeated_data_no_interleave.meta_info == {"info": "test_info"}


def test_dataproto_pad_unpad():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=2)
    assert pad_size == 1

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2]])
    expected_labels = ["a", "b", "c", "a"]

    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=3)
    assert pad_size == 0

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    expected_labels = ["a", "b", "c"]

    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}

    padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=7)
    assert pad_size == 4

    expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]])
    expected_labels = ["a", "b", "c", "a", "b", "c", "a"]
    assert torch.all(torch.eq(padded_data.batch["obs"], expected_obs))
    assert (padded_data.non_tensor_batch["labels"] == expected_labels).all()
    assert padded_data.meta_info == {"info": "test_info"}

    unpadd_data = unpad_dataproto(padded_data, pad_size=pad_size)
    assert torch.all(torch.eq(unpadd_data.batch["obs"], obs))
    assert (unpadd_data.non_tensor_batch["labels"] == labels).all()
    assert unpadd_data.meta_info == {"info": "test_info"}


def test_dataproto_fold_unfold():
    from verl.protocol import DataProto, fold_batch_dim, unfold_batch_dim

    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    data1 = data.repeat(repeat_times=2, interleave=True)

    data2 = fold_batch_dim(data1, new_batch_size=3)

    torch.testing.assert_close(data2.batch["obs"], torch.tensor([[[1, 2], [1, 2]], [[3, 4], [3, 4]], [[5, 6], [5, 6]]]))
    assert (data2.non_tensor_batch["labels"] == [["a", "a"], ["b", "b"], ["c", "c"]]).all()

    data2.reorder(indices=torch.tensor([1, 2, 0]))

    data3 = unfold_batch_dim(data2, batch_dims=2)

    torch.testing.assert_close(data3.batch["obs"], torch.tensor([[3, 4], [3, 4], [5, 6], [5, 6], [1, 2], [1, 2]]))
    assert (data3.non_tensor_batch["labels"] == ["b", "b", "c", "c", "a", "a"]).all()
    assert data3.meta_info == {"info": "test_info"}


def test_torch_save_data_proto():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = ["a", "b", "c"]
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})
    data.save_to_disk("test_data.pt")
    loaded_data = DataProto.load_from_disk("test_data.pt")

    assert torch.all(torch.eq(loaded_data.batch["obs"], data.batch["obs"]))
    assert (loaded_data.non_tensor_batch["labels"] == data.non_tensor_batch["labels"]).all()
    assert loaded_data.meta_info == data.meta_info

    import os

    os.remove("test_data.pt")


def test_len():
    obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
    labels = np.array(["a", "b", "c"], dtype=object)
    data = DataProto.from_dict(tensors={"obs": obs}, non_tensors={"labels": labels}, meta_info={"info": "test_info"})

    assert len(data) == 3

    data = DataProto(batch=None, non_tensor_batch={"labels": labels}, meta_info={"info": "test_info"})

    assert len(data) == 3

    data = DataProto(batch=None, non_tensor_batch={}, meta_info={"info": "test_info"})

    assert len(data) == 0

    data = DataProto(batch=None, non_tensor_batch=None, meta_info={"info": "test_info"})

    assert len(data) == 0


def test_seqlen_balancing():
    from verl.utils.seqlen_balancing import get_reverse_idx, rearrange_micro_batches

    input_ids = torch.randint(low=0, high=10, size=(20, 100))
    from verl.utils.model import create_random_mask

    attention_mask = create_random_mask(
        input_ids=input_ids, max_ratio_of_left_padding=0.1, max_ratio_of_valid_token=0.9, min_ratio_of_valid_token=0.5
    )
    data = {"input_ids": input_ids, "attention_mask": attention_mask}
    dataproto = DataProto.from_single_dict(data)
    micro_batches, micro_bsz_idx_lst = rearrange_micro_batches(dataproto.batch, max_token_len=300)
    batch = torch.cat(micro_batches)
    micro_bsz_idx = []
    for idx in micro_bsz_idx_lst:
        micro_bsz_idx.extend(idx)
    reverse_idx_map = get_reverse_idx(micro_bsz_idx)
    reverse_idx_map = torch.tensor(reverse_idx_map)
    new_batch = batch[reverse_idx_map]
    torch.testing.assert_close(new_batch, dataproto.batch)
